--- description: How to make scatter plots in Python with Plotly. display_as: basic has_thumbnail: True ipynb: ~notebook_demo/2 language: python layout: user-guide name: Scatter Plots order: 2 page_type: example_index permalink: python/line-and-scatter/ redirect_from: python/line-and-scatter-plots-tutorial/ thumbnail: thumbnail/line-and-scatter.jpg title: Python Scatter Plots | plotly v4upgrade: True --- {% raw %}
Plotly Express functions take as a first argument a tidy pandas.DataFrame. With px.scatter, each data point is represented as a marker point, which location is given by the x and y columns.
import plotly.express as px
iris = px.data.iris()
fig = px.scatter(iris, x="sepal_width", y="sepal_length")
fig.show()
Note that color and size data are added to hover information. You can add other columns to hover data with the hover_data argument of px.scatter.
import plotly.express as px
iris = px.data.iris()
fig = px.scatter(iris, x="sepal_width", y="sepal_length", color="species",
size='petal_length', hover_data=['petal_width'])
fig.show()
import plotly.express as px
gapminder = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(gapminder, x='year', y='lifeExp', color='country')
fig.show()
When data are not available as tidy dataframes, it is possible to use the more generic go.Scatter function from plotly.graph_objects. Whereas plotly.express has two functions scatter and line, go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode. The different options of go.Scatter are documented in its reference page.
import plotly.graph_objects as go
import numpy as np
N = 1000
t = np.linspace(0, 10, 100)
y = np.sin(t)
fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))
fig.show()
Use mode argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the line charts notebook and the filled area plots notebook.
import plotly.graph_objects as go
# Create random data with numpy
import numpy as np
N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N) + 5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 5
fig = go.Figure()
# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
mode='markers',
name='markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
mode='lines+markers',
name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
mode='lines',
name='lines'))
fig.show()
In bubble charts, a third dimension of the data is shown through the size of markers. For more examples, see the bubble chart notebook
import plotly.graph_objects as go
fig = go.Figure(data=go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
mode='markers',
marker=dict(size=[40, 60, 80, 100],
color=[0, 1, 2, 3])
))
fig.show()
import plotly.graph_objects as go
import numpy as np
t = np.linspace(0, 10, 100)
fig = go.Figure()
fig.add_trace(go.Scatter(
x=t, y=np.sin(t),
name='sin',
mode='markers',
marker_color='rgba(152, 0, 0, .8)'
))
fig.add_trace(go.Scatter(
x=t, y=np.cos(t),
name='cos',
marker_color='rgba(255, 182, 193, .9)'
))
# Set options common to all traces with fig.update_traces
fig.update_traces(mode='markers', marker_line_width=2, marker_size=10)
fig.update_layout(title='Styled Scatter',
yaxis_zeroline=False, xaxis_zeroline=False)
fig.show()
import plotly.graph_objects as go
import pandas as pd
data= pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv")
fig = go.Figure(data=go.Scatter(x=data['Postal'],
y=data['Population'],
mode='markers',
marker_color=data['Population'],
text=data['State'])) # hover text goes here
fig.update_layout(title='Population of USA States')
fig.show()
import plotly.graph_objects as go
import numpy as np
fig = go.Figure(data=go.Scatter(
y = np.random.randn(500),
mode='markers',
marker=dict(
size=16,
color=np.random.randn(500), #set color equal to a variable
colorscale='Viridis', # one of plotly colorscales
showscale=True
)
))
fig.show()
Now in Ploty you can implement WebGL with Scattergl() in place of Scatter()
for increased speed, improved interactivity, and the ability to plot even more data!
import plotly.graph_objects as go
import numpy as np
N = 100000
fig = go.Figure(data=go.Scattergl(
x = np.random.randn(N),
y = np.random.randn(N),
mode='markers',
marker=dict(
color=np.random.randn(N),
colorscale='Viridis',
line_width=1
)
))
fig.show()
import plotly.graph_objects as go
import numpy as np
N = 100000
r = np.random.uniform(0, 1, N)
theta = np.random.uniform(0, 2*np.pi, N)
fig = go.Figure(data=go.Scattergl(
x = r * np.cos(theta), # non-uniform distribution
y = r * np.sin(theta), # zoom to see more points at the center
mode='markers',
marker=dict(
color=np.random.randn(N),
colorscale='Viridis',
line_width=1
)
))
fig.show()
Dash is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its source code can easily be deployed to a PaaS.
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-linescatterplot/", width="100%",height="750px", frameBorder="0")
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-linescatterplot/code", width="100%",height=500, frameBorder="0")
See https://plot.ly/python/reference/#scatter or https://plot.ly/python/reference/#scattergl for more information and chart attribute options!